Quantum transport at the Dirac point: Mapping out the minimum conductivity from pristine to disordered graphene

The phase space for graphene's minimum conductivity $\sigma_\mathrm{min}$ is mapped out using Landauer theory modified for scattering using Fermi's Golden Rule, as well as the Non-Equilibrium Green's Function (NEGF) simulation with a Monte Carlo sampling over impurity distributions. The resulting `fan diagram' spans the range from ballistic to diffusive over varying aspect ratios ($W/L$), and bears several surprises. {The device aspect ratio determines how much tunneling (between contacts) is allowed and becomes the dominant factor for the evolution of $\sigma_{min}$ from ballistic to diffusive regime. We find an increasing (for $W/L>1$) or decreasing ($W/L<1$) trend in $\sigma_{min}$ vs. impurity density, all converging around $128q^2/\pi^3h\sim 4q^2/h$ at the dirty limit}. In the diffusive limit, the {conductivity} quasi-saturates due to the precise cancellation between the increase in conducting modes from charge puddles vs the reduction in average transmission from scattering at the Dirac Point. In the clean ballistic limit, the calculated conductivity of the lowest mode shows a surprising absence of Fabry-P\'{e}rot oscillations, unlike other materials including bilayer graphene. We argue that the lack of oscillations even at low temperature is a signature of Klein tunneling

Mortality in dementia with Lewy bodies compared with Alzheimer's dementia: a retrospective naturalistic cohort study.

OBJECTIVES: To use routine clinical data to investigate survival in dementia with Lewy bodies (DLB) compared with Alzheimer's dementia (AD). DLB is the second most common dementia subtype after AD, accounting for around 7% of dementia diagnoses in secondary care, though studies suggest that it is underdiagnosed by up to 50%. Most previous studies of DLB have been based on select research cohorts, so little is known about the outcome of the disease in routine healthcare settings. SETTING: Cambridgeshire & Peterborough NHS Foundation Trust, a mental health trust providing secondary mental health care in England. SAMPLE: 251 DLB and 222 AD identified from an anonymised database derived from electronic clinical case records across an 8-year period (2005-2012), with mortality data updated to May 2015. RESULTS: Raw (uncorrected) median survival was 3.72 years for DLB (95% CI 3.33 to 4.14) and 6.95 years for AD (95% CI 5.78 to 8.12). Controlling for age at diagnosis, comorbidity and antipsychotic prescribing the model predicted median survival for DLB was 3.3 years (95% CI 2.88 to 3.83) for males and 4.0 years (95% CI 3.55 to 5.00) for females, while median survival for AD was 6.7 years (95% CI 5.27 to 8.51) for males and 7.0 years (95% CI 5.92 to 8.73) for females. CONCLUSION: Survival from first presentation with cognitive impairment was markedly shorter in DLB compared with AD, independent of age, sex, physical comorbidity or antipsychotic prescribing. This finding, in one of the largest clinical cohorts of DLB cases assembled to date, adds to existing evidence for poorer survival for DLB versus AD. There is an urgent need for further research to understand possible mechanisms accounting for this finding

Dual-Stream Attention Transformers for Sewer Defect Classification

We propose a dual-stream multi-scale vision transformer (DS-MSHViT) architecture that processes RGB and optical flow inputs for efficient sewer defect classification. Unlike existing methods that combine the predictions of two separate networks trained on each modality, we jointly train a single network with two branches for RGB and motion. Our key idea is to use self-attention regularization to harness the complementary strengths of the RGB and motion streams. The motion stream alone struggles to generate accurate attention maps, as motion images lack the rich visual features present in RGB images. To facilitate this, we introduce an attention consistency loss between the dual streams. By leveraging motion cues through a self-attention regularizer, we align and enhance RGB attention maps, enabling the network to concentrate on pertinent input regions. We evaluate our data on a public dataset as well as cross-validate our model performance in a novel dataset. Our method outperforms existing models that utilize either convolutional neural networks (CNNs) or multi-scale hybrid vision transformers (MSHViTs) without employing attention regularization between the two streams

Health Risk Assessment Associated with Norovirus Incidence in Raw Wastewater in Jeddah, Saudi Arabia

Abstract: Norovirus caused an epidemic gastroenteritis in humans. It can be transmitted by the fecaloral and the aerosol route. Norovirus represent a most common cause of acute gastroenteritis which responsible about 42%-96% of nonbacterial gastroenteritis worldwide. Current study aims to detect a norovirus in Jeddah wastewater. A total one hundred of wastewater samples were collected from outlet of Al-Misk Lake east of Jeddah city over a period of fourteen months from January 2009 to February 2010. All samples were filtered and virus concentrated and screened for GII human. A molecular inhouse detection was performed using nRT-PCR. The most conserved regions; N32, N33, N35 and N36 were used for primers design. Of 19 positive samples were signaled a band of 338bp. In conclusion, this study revealed that the norovirus was frequently present in Jeddah wastewater, which should be alert to do not use this water in land irrigation